{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 实验辅助"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实验 2-理论题作答\n",
    "\n",
    "总开销 E (千元)   \n",
    "总月数 m  \n",
    "自己开发策略总开销: E1 = 30 + 3.5m  \n",
    "购买软件公司产品策略总开销: E2 = 18 + 4.2m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "m = list(range(1, 20, 1))\n",
    "E1 = [30 + 3.5 * i for i in m]\n",
    "E2 = [18 + 4.2 * i for i in m]\n",
    "x1 = 12 / 0.7\n",
    "y1 = 30 + 3.5 * x1\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.xticks(m)\n",
    "plt.plot(m, E1)\n",
    "plt.plot(m, E2)\n",
    "plt.scatter(x1, y1)\n",
    "plt.axvline(x1, color='magenta', linestyle='--', linewidth=2)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实验 7-项目的关系依赖矩阵\n",
    "\n",
    "![image-20211105143328340](http:cdn.ayusummer233.top/img/202111051433476.png)  \n",
    "![image-20211105144906584](http:cdn.ayusummer233.top/img/202111051449702.png)  \n",
    "![image-20211105145006727](http://cdn.ayusummer233.top/img/202111051450794.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析: 时间成本上由最长的路径 t_1 -> t_3 -> t_5 -> t_6 -> t_7 决定, 为 5 个单位时间\n",
    "- `时刻 1`: $t_1$ 作为整个项目的首个任务成本最低的选择为技能完全对口的 $e_3$, \\$60  \n",
    "- `时刻 2`: \n",
    "  - $t_2$ 成本最低的选择为 $e_3$  \\$60   \n",
    "  - $t_3$ 成本最低的选择为技能完全对口的 $e_4$  \\$50  \n",
    "- `时刻 3`:\n",
    "  - $t_4$ 成本最低的选择为 $e_4$  \\$50  \n",
    "  - $t_5$ 成本最低的选择为 $e_2$  \\$80  \n",
    "- `时刻 4`: $t_6$ 成本最低的选择为 $e_3$  \\$60  \n",
    "- `时刻 5`: $t_7$ 成本最低的选择为 $e_4$  \\$50\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实验 8 - 计算 ROI\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NPV1 = 618.0, NPV2 = -179770.0, NPV3 = 13721.0, NPV4 = 21661.5\n"
     ]
    }
   ],
   "source": [
    "x1 = [-100000, 10000, 10000, 10000, 20000, 100000]\n",
    "x2 = [-1000000, 200000, 200000, 200000, 200000, 300000]\n",
    "x3 = [-100000, 30000, 30000, 30000, 30000, 30000]\n",
    "x4 = [-120000, 30000, 30000, 30000, 30000, 75000]\n",
    "def f(x):\n",
    "    return x[0] + x[1] * 0.9091 + x[2] * 0.8264 + x[3] * 0.7513 + x[4] * 0.6830 + x[5] * 0.6209\n",
    "print(\"NPV1 = {0}, NPV2 = {1}, NPV3 = {2}, NPV4 = {3}\".format(f(x1), f(x2), f(x3), f(x4)))"
   ]
  }
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